System and method for automatic treatment planning

ABSTRACT

The present disclosure relates to systems, methods, and computer-readable storage media for radiotherapy. Embodiments of the present disclosure may receive a plurality of training data and determine one or more predictive models based on the training data. The one or more predictive models may be determined based on at least one of a conditional probability density associated with a selected output characteristic given one or more selected input variables or a joint probability density. Embodiments of the present disclosure may also receive patient specific testing data. In addition, embodiments of the present disclosure may predict a probability density associated with a characteristic output based on the one or more predictive models and the patient specific testing data. Moreover, embodiments of the present disclosure may generate a new treatment plan based on the prediction and may use the new treatment plan to validate a previous treatment plan.

TECHNICAL FIELD

This disclosure relates generally to radiation therapy or radiotherapy.More specifically, this disclosure relates to systems and methods fortraining and/or predicting data for use in developing a radiationtherapy treatment plan to be used during radiotherapy.

BACKGROUND

Radiotherapy is used to treat cancers and other ailments in mammalian(e.g., human and animal) tissue. One such radiotherapy technique is aGamma Knife, by which a patient is irradiated by a large number oflow-intensity gamma rays that converge with high intensity and highprecision at a target (e.g., a tumor). In another embodiment,radiotherapy is provided using a linear accelerator, whereby a tumor isirradiated by high-energy particles (e.g., electrons, protons, ions andthe like). The placement and dose of the radiation beam must beaccurately controlled to ensure the tumor receives the prescribedradiation, and the placement of the beam should be such as to minimizedamage to the surrounding healthy tissue, often called the organ(s) atrisk (OARs).

Traditionally, for each patient, a radiation therapy treatment plan(“treatment plan”) may be created using an optimization technique basedon clinical and dosimetric objectives and constraints (e.g., themaximum, minimum, and mean doses to the tumor and critical organs). Thetreatment planning procedure may include using a three-dimensional imageof the patient to identify a target region (e.g., the tumor) and toidentify critical organs near the tumor. Creation of a treatment plancan be a time consuming process where a planner tries to comply withvarious treatment objectives or constraints (e.g., dose volume histogram(DVH) objectives), taking into account their individual importance(e.g., weighting) in order to produce a treatment plan which isclinically acceptable. This task can be a time-consuming trial-and-errorprocess that is complicated by the various organs at risk (OARs, becauseas the number of OARs increases (e.g., up to thirteen for ahead-and-neck treatment), so does the complexity of the process. OARsdistant from a tumor may be easily spared from radiation, while OARsclose to or overlapping a target tumor may be difficult to spare.Segmentation may be performed to identify the OARs and the area to betreated, for example, a planning target volume (PTV). Aftersegmentation, a dose plan may be created for the patient indicating thedesirable amount of radiation to be received by the PTV (e.g., target)and/or the OARs. The PTV may have an irregular volume and may be uniqueas to its size, shape and position. A treatment plan can be calculatedafter optimizing a large number of plan parameters to ensure that themaximum dose is provided to the PTV while as low a dose as possible isprovided to surrounding healthy tissue. Therefore, a radiation therapytreatment plan may be determined by balancing efficient control of thedose to treat the tumor against sparing any OAR. Typically, the qualityof a radiation treatment plan may depend upon the level of experience ofthe planner. Further complications may be caused by anatomicalvariations between patients.

Currently, most treatment planning procedures limit the parametersconsidered to those associated with the specific patient or to thespecific treatment session. Experience generated from previouslydeveloped treatment plans for the same patient, or similar treatmentprocedures for patients having the same kind of tumor with similar sizeand location taking into account potential outcomes (e.g., dose applied,success rate, survival time and the like), however, has not beeneffectively used in the procedures of developing new plans. What isneeded is the ability to utilize previous treatment plans to predictobjective parameters for one or more outcomes that may be used togenerate a radiation therapy treatment plan, which may provide anoptimized dose to be delivered to treat the tumor, while minimizingexposure to the one or more OARs.

SUMMARY

Certain embodiments of the present disclosure relate to a radiotherapysystem. The radiotherapy system may comprise a memory storing computerexecutable instructions and a processor device communicatively coupledto the memory. The processor device may be configured to execute thecomputer executable instructions for receiving a plurality of trainingdata and determining one or more predictive models based on the trainingdata. The one or more predictive models may be determined based on atleast one of a conditional probability density associated with aselected output characteristic given one or more selected inputvariables or a joint probability density. The processor device may alsobe configured to execute the computer executable instructions forreceiving patient specific testing data. In addition, the processordevice may be configured to execute the computer executable instructionsfor predicting a probability density associated with a characteristicoutput based on the one or more predictive models and the patientspecific testing data. Moreover, the processor device may be configuredto execute the computer executable instructions for generating a newtreatment plan based on the prediction.

Certain embodiments of the present disclosure relate to a method forprediction in a radiotherapy system. The method may be implemented by aprocessor device executing a plurality of computer executableinstructions. The method may comprise receiving a plurality of trainingdata. The training data may include a plurality of training samples.Each of the training samples may comprise a feature vector and an outputvector. The method may also comprise determining a joint probabilitydensity associated with the feature vector and the corresponding outputvector. In addition, the method may comprise generating one or morepredictive models based on the joint probability density and storing theone or more predictive models in a memory. The method may also comprisereceiving a plurality of patient specific testing data. The patientspecific testing data may comprise a plurality of testing samples. Themethod may also comprise determining a probability density for a featurevector associated with each testing sample of the patient specifictesting data. In addition, the method may comprise predicting aprobability density for an output vector associated with each testingsample of the patient specific testing data using (1) the probabilitydensity for the feature vector associated with the patient specifictesting data and (2) the one or more predictive models. Moreover, themethod may comprise generating a new treatment plan based on theprediction.

Certain embodiments of the present disclosure relate to a non-transitorycomputer-readable storage medium having computer-executable instructionsstored thereon. The computer-executable instructions, when executed by aprocessor device, may direct the processor device to receive a pluralityof training data. The training data may include a plurality of trainingsamples. Each of the training samples may comprise a feature vector andan output vector. The computer-executable instructions may also directthe processor device to determine a joint probability density associatedwith the feature vector and the corresponding output vector anddetermine a conditional probability density associated with the outputvector given the feature vector. In addition, the computer-executableinstructions may direct the processor device to generate one or morepredictive models based on at least one of the joint probability densityor the conditional probability density and to store the one or morepredictive models in a memory. Moreover, the computer-executableinstructions may direct the processor device to receive a plurality ofpatient specific testing data. The patient specific testing data maycomprise a plurality of testing samples. The computer-executableinstructions may also direct the processor device to determine aprobability density associated for a feature vector associated with eachtesting sample of the patient specific testing data. In addition, thecomputer-executable instructions may direct the processor device topredict a probability density of an output vector associated with eachtesting sample of the patient specific testing data using (1) theprobability density for the feature vector associated with the patientspecific testing data and (2) the one or more predictive models.Moreover, the computer-executable instructions may direct the processordevice to generate a new treatment plan based on the prediction andvalidate a previous treatment plan based on the new treatment plan.

Additional objects and advantages of the present disclosure will be setforth in part in the following detailed description, and in part will beobvious from the description, or may be learned by practice of thepresent disclosure. The objects and advantages of the present disclosurewill be realized and attained by means of the elements and combinationsparticularly pointed out in the appended claims.

It is to be understood that the foregoing general description and thefollowing detailed description are exemplary and explanatory only, andare not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which constitute a part of thisspecification, illustrate several embodiments and, together with thedescription, serve to explain the disclosed principles.

FIG. 1 illustrates an exemplary radiotherapy system, according to someembodiments of the present disclosure.

FIG. 2A illustrates a radiotherapy device, a Gamma Knife, according tosome embodiments of the present disclosure.

FIG. 2B illustrates another radiotherapy device, a linear accelerator,according to some embodiments of the present disclosure.

FIG. 2C illustrates a data processing device and a database used in aradiotherapy system, according to some embodiments of the presentdisclosure.

FIG. 3A illustrates a target tumor and an OAR, according to someembodiments of the present disclosure.

FIG. 3B illustrates an exemplary dose-volume histogram (DVH) for atarget and an exemplary DVH for an OAR, according to some embodiments ofthe present disclosure.

FIG. 4 is a flowchart illustrating an exemplary method of a datatraining process and a prediction process, according to some embodimentsof the present disclosure.

FIG. 5 is a flowchart illustrating an exemplary method of utilizingpatient specific testing data, according to some embodiments of thepresent disclosure.

DETAILED DESCRIPTION

Exemplary embodiments are described with reference to the accompanyingdrawings. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears.Wherever convenient, the same reference numbers are used throughout thedrawings to refer to the same or like parts. While examples and featuresof disclosed principles are described herein, modifications,adaptations, and other implementations are possible without departingfrom the spirit and scope of the disclosed embodiments. Also, the words“comprising,” “having,” “containing,” and “including,” and other similarforms are intended to be equivalent in meaning and be interpreted asopen ended, in that, an item or items following any one of these wordsis not meant to be an exhaustive listing of such item or items, or meantto be limited to only the listed item or items. And the singular forms“a,” “an,” and “the” are intended to include plural references, unlessthe context clearly dictates otherwise.

Systems and methods consistent with the present disclosure are directedto generating a radiotherapy treatment plan or validating a radiotherapytreatment plan using statistical information derived from past orprevious treatment plans. As used herein, a past/previous treatment planrefers to a plan for a radiotherapy treatment of the same patient or adifferent patient that was conducted any time before the currenttreatment was generated. For instance, in the case of adaptiveradiotherapy, a treatment plan may be initially prepared for a patient,but for each fraction of treatment the plan may be updated; therefore,any plan created prior to the update may be considered as apast/previous treatment plan. The use of the statistical information mayimprove consistency, accuracy, and efficiency in the treatment planningprocess because similarities between the new and past plans can be drawnand utilized. For example, patients having the same kind of tumors withsimilar size and located at similar body part (e.g., prostate, head andneck, lung, brain, and the like) may share similar treatment procedures.Past treatment plans may provide valuable information regarding the linkbetween observations (e.g., kind of tumor, size of tumor, or location ofthe tumor) and parameters/outcomes in the past treatments (e.g., doseapplied, success rate, survival time, and the like).

A training module may use the information of the past treatment plans toderive statistical estimations of various parameters and/orrelationships among these parameters. A prediction module may then usethe one or more predictive modules to predict one or more objectiveparameters (e.g., outcomes) that can be used to develop a treatmentplan. As used herein, training data may refer to information regardingthe past treatment plans; predictive models refer to statisticalestimations or derivations drawn or calculated from the past treatmentplans; testing data refer to information regarding the new treatmentplan; prediction data refer to predictions of parameters or likelyoutcomes of the new treatment plan.

FIG. 1 illustrates an exemplary radiotherapy system 100, according tosome embodiments of the present disclosure. Radiotherapy system 100 mayinclude a training module 112, a prediction module 114, a trainingdatabase 122, a testing database 124, a radiotherapy device 130, and animage acquisition device 140. Radiotherapy system 100 may also beconnected to a treatment planning system (TPS) 142 and an oncologyinformation system (OIS) 144, which may provide patient information. Inaddition, radiotherapy system 100 may include a display device and auser interface (not shown).

As shown in FIG. 1, training module 112 may communicate with trainingdatabase 122 to receive training data. The training data stored intraining database 122 may be obtained from a treatment planning system142, which may store data of previous radiotherapy treatment sessions(e.g., treatment planning system 142 may store previously developedtreatment plans for a particular patient to be treated and for otherpatients, as well as other radiotherapy information). For example,treatment planning system 142 may provide information about a particulardose to be applied to a patient and other radiotherapy relatedinformation (e.g., type of therapy: such as image guided radiationtherapy (IGRT), intensity modulated radiation therapy (IMRT),stereotactic radiotherapy; the number of beams; the beam angles; thedose per beam; and the like). In addition, the training data may alsoinclude image data to be obtained from image acquisition device 140. Forexample, image acquisition device 140 may provide medical images (e.g.,Magnetic Resonance Imaging (MRI) images, 3D MRI, 2D streaming MRI, 4Dvolumetric MRI, Computed Tomography (CT) images, Cone-Beam CT, PositronEmission Tomography (PET) images, functional MRI images (e.g., fMRI,DCE-MRI and diffusion MRI), X-ray images, fluoroscopic image, ultrasoundimages, radiotherapy portal images, single-photo emission computedtomography (SPECT) images, and the like) of a patient. In someembodiments, the training data may be collected from an OncologyInformation System (OIS 144 (e.g., patient information, medical labresults, and the like).

Training module 112 may use the training data received from trainingdatabase 122 to generate trained data. The trained data may be used todetermine a prediction model that may be utilized by the predictionmodule 114. As described above, prediction model may refer toderivations drawn or calculated from the past treatment plans. Inaddition, prediction model may include, for example, a conditionalprobability of an outcome (e.g., a certain dose received by a spatialvolume or a voxel) given an observation of a certain property (e.g., adistance between the voxel and the boundary of a target such as atumor). In another example, prediction model may include a conditionalprobability of a certain survival time given a tumor size.

Prediction module 114 may receive the one or more prediction models fromtraining module 112 and use the one or more prediction models to predictcertain objective parameters, such as properties or outcomes, in orderto generate a new treatment plan. For example, prediction module 114 mayreceive testing data from testing database 124. The testing data mayinclude information such as imaging data (e.g., MRI, CT, X-ray, PET,SPECT, and the like), organ or volume of interest segmentation data,functional organ modeling data (e.g., serial versus parallel organs, andappropriate dose response models), radiation dosage (e.g., alsoincluding dose-volume histogram (DVH) information), lab data (e.g.,hemoglobin, platelets, cholesterol, triglycerides, creatinine, sodium,glucose, calcium, weight), vital signs (blood pressure, temperature,respiratory rate and the like), genomic data (e.g., genetic profiling),demographics (age, sex, ethnicity), other diseases affecting the patient(e.g., cardiovascular disease, respiratory disease, diabetes, radiationhypersensitivity syndromes, and the like), medications and drugreactions, diet and lifestyle (e.g., smoking or non-smoking),environmental risk factors, tumor characteristics (histological type,tumor grade, hormone and other receptor status, tumor size, vascularitycell type, cancer staging, gleason score), previous treatments (e.g.,surgeries, radiation, chemotherapy, hormone therapy), lymph node anddistant metastases status, genetic/protein biomarkers (e.g., such asMYC, GADD45A, PPM1D, BBC3, CDKN1A, PLK3, XPC, AKT1, RELA, BCL2L1, PTEN,CDK1, XIAP, and the like), single nucleotide polymorphisms (SNP)analysis (e.g., XRCC1, XRCC3, APEX1, MDM2, TNFR, MTHFR, MTRR, VEGF,TGFβ, TNFα), and the like.

The testing data stored in testing database 124 may further includeimage data that may be obtained from image acquisition device 140. Forexample, image acquisition device 140 may provide medical images (e.g.,MRI images, CT images, PET images, MRI images, X-ray images, ultrasoundimages, radiotherapy portal images, single-photo emission computedtomography (SPECT) images, and the like) of the new patient.

The testing data, as described above, and other radiotherapy informationstored in testing database 124 may also be obtained from treatmentplanning system 142 and oncology information system 144. Testing datamay be stored in testing database 124 before it is received byprediction module 114.

Alternatively, during adaptive radiotherapy, the testing data may bereceived by prediction module 114 directly from radiotherapy device 130.In some embodiments, testing data may be retrieved from radiotherapydevice 130 in an online mode, e.g., while radiotherapy device 130 is inactive operation of performing radiotherapy treatment (e.g., actual dosedelivered to a patient). In other embodiments, testing data may beretrieved from radiotherapy device 130 in an offline mode, e.g., whileradiotherapy device 130 is not in active operation of performingradiotherapy treatment.

After prediction module 114 generates a plurality of objectiveparameters based on the testing data and the prediction model, theplurality of objective parameters may be used to develop a treatmentplan. The developed treatment plan may be for a patient currentlyundergoing radiotherapy (e.g., the treatment plan may be updated(adapted) based on current parameters). Alternatively, the developedtreatment plan may be for a new patient. The treatment plan may be usedby radiotherapy device 130 to perform a treatment in accordance with thetreatment plan.

In some embodiments, radiotherapy device 130 may be local with respectto prediction module 114. For example, radiotherapy device 130 andprediction module 114 may be located in the same room of a medicalfacility/clinic. In other embodiments, radiotherapy device 130 may beremote with respect to prediction module 114 and the data communicationbetween radiotherapy device 130 and prediction module 114 via thetreatment planning system 142 may be carried out through a network(e.g., a local area network (LAN); a wireless network; a cloud computingenvironment such as software as a service, platform as a service,infrastructure as a service; a client-server; a wide area network (WAN);and the like). Similarly, the communication links between trainingmodule 112 and training database 122, between training module 112 andprediction module 114, between prediction module 114 and testingdatabase 124, between testing database 124 and treatment planning system142, between training database 122 and oncology information system 144,between treatment planning system 142 and oncology information system144, between treatment planning system 142 and radiation therapy device130, between image acquisition device 140 and testing database 124,between image acquisition device 140 and treatment planning system 142,between image acquisition device and training database 122, may also beimplemented in a local or remote manner.

In some embodiments, training module 112 and prediction module 114 maybe implemented in a single data processing device 110. For example,training module 112 and prediction module 114 may be implemented asdifferent software programs operating on the same hardware device, aswill be described in greater detail later with respect to FIG. 2C.Similarly, training database 122 and testing database 124 may beimplemented as a single database 120. For example, a single database maystore both the training data and testing data. It is contemplated thatany one of training module 112, prediction module 114, training database122, and testing database 124 may be implemented as a standalone module.

Image acquisition device 140 may include an MRI imaging device, a CTimaging device, a PET imaging device, an ultrasound device, afluoroscopic device, a SPECT imaging device, or other medical imagingdevices for obtaining one or more medical images of a patient. Imageacquisition device 140 may provide the medical images to treatmentplanning system 142, testing database 124, and/or training database 122.

FIG. 2A illustrates an example of one type of radiotherapy device 130(e.g., Leksell Gamma Knife), according to some embodiments of thepresent disclosure. As shown in FIG. 2A, in a radiotherapy treatmentsession, a patient 202 may wear a coordinate frame 220 to keep stablethe patient's body part (e.g., the head) undergoing surgery orradiotherapy. Coordinate frame 220 and a patient positioning system 222may establish a spatial coordinate system, which may be used whileimaging a patient or during radiation surgery. Radiotherapy device 130may include a protective housing 214 to enclose a plurality of radiationsources 212. Radiation sources 212 may generate a plurality of radiationbeams (e.g., beamlets) through beam channels 216. The plurality ofradiation beams may be configured to focus on an isocenter 218 fromdifferent directions. While each individual radiation beam may have arelatively low intensity, isocenter 218 may receive a relatively highlevel of radiation when multiple doses from different radiation beamsaccumulate at isocenter 218. In certain embodiments, isocenter 218 maycorrespond to a target under surgery or treatment, such as a tumor.

FIG. 2B illustrates another example of a radiotherapy device 130 (e.g.,a linear accelerator 10), according to some embodiments of the presentdisclosure. Using a linear accelerator 10, a patient 42 may bepositioned on a patient table 43 to receive the radiation dosedetermined by the treatment plan. Linear accelerator 10 may include aradiation head 45 that generates a radiation beam 46. The entireradiation head 45 may be rotatable around a horizontal axis 47. Inaddition, below the patient table 43 there may be provided a flat panelscintillator detector 44, which may rotate synchronously with radiationhead 45 around an isocenter 41. The intersection of the axis 47 with thecenter of the beam 46, produced by the radiation head 45, is usuallyreferred to as the “isocenter”. The patient table 43 may be motorized sothat the patient 42 can be positioned with the tumor site at or close tothe isocenter 41. The radiation head 45 may rotate about a gantry 47, toprovide patient 42 with a plurality of varying dosages of radiationaccording to the treatment plan.

FIG. 2C illustrates an embodiment of data processing device 110 that iscommunicatively coupled to a database 120 and a hospital database 121.As shown in FIG. 2C, data processing device 110 may include a processor250, a memory or storage device 260, and a communication interface 270.Memory/storage device 260 may store computer executable instructions,such as an operating system 262, training/prediction software 264,treatment planning software 265, and any other computer executableinstructions to be executed by the processor 250.

Processor 250 may be communicatively coupled to a memory/storage device260 and configured to execute the computer executable instructionsstored thereon. For example, processor 250 may executetraining/prediction software 264 to implement functionalities oftraining module 112 and/or prediction module 114. In addition, processordevice 250 may execute treatment planning software 265 (e.g., such asMonaco® software manufactured by Elekta) that may interface withtraining/prediction software 264.

Processor 250 may communicate with database 120 through communicationinterface 270 to send/receive data to/from database 120. Database 120may include one or both of training database 122 and testing database124. One skilled in the art would appreciate that database 120 mayinclude a plurality of devices located either in a central ordistributed manner. In addition, processor 250 may communicate with thehospital database 121 to implement functionalities of oncologyinformation system 144 as shown in FIG. 1.

Processor 250 may be a processing device, include one or moregeneral-purpose processing devices such as a microprocessor, centralprocessing unit (CPU), graphics processing unit (GPU), or the like. Moreparticularly, processor device 250 may be a complex instruction setcomputing (CISC) microprocessor, reduced instruction set computing(RISC) microprocessor, very long instruction Word (VLIW) microprocessor,a processor implementing other instruction sets, or processorsimplementing a combination of instruction sets. Processor 250 may alsobe one or more special-purpose processing devices such as an applicationspecific integrated circuit (ASIC), a field programmable gate array(FPGA), a digital signal processor (DSP), a System on a Chip (SoC), orthe like. As would be appreciated by those skilled in the art, in someembodiments, processor 250 may be a special-purpose processor, ratherthan a general-purpose processor.

Memory/storage device 260 may include a read-only memory (ROM), a flashmemory, a random access memory (RAM), a static memory, etc. In someembodiments, memory/storage device 260 may include a machine-readablestorage medium. While the machine-readable storage medium in anembodiment may be a single medium, the term “machine-readable storagemedium” should be taken to include a single medium or multiple media(e.g., a centralized or distributed database, and/or associated cachesand servers) that store the one or more sets of computer executableinstructions or data. The term “machine-readable storage medium” shallalso be taken to include any medium that is capable of storing orencoding a set of instructions for execution by the machine and thatcause the machine to perform any one or more of the methodologies of thepresent disclosure. The term “machine readable storage medium” shallaccordingly be taken to include, but not be limited to, solid-statememories, optical and magnetic media.

Communication interface 270 may include a network adaptor, a cableconnector, a serial connector, a USB connector, a parallel connector, ahigh-speed data transmission adaptor such as fiber, USB 3.0,thunderbolt, and the like, a wireless network adaptor such as a WIFIadaptor, a telecommunication (3G, 4G/LTE and the like) adaptor, and thelike. The communication interface 270 may provide the functionality of alocal area network (LAN), a wireless network, a cloud computingenvironment (e.g., software as a service, platform as a service,infrastructure as a service), a client-server, a wide area network(WAN), and the like. Processor 250 may communicate with database 120 orother devices or systems via communication interface 270.

In a radiotherapy treatment, generating the treatment plan may includethe delineation of a target, such as a tumor. In some embodiments, thedelineation of one or more OARs, healthy tissue surrounding the tumor orin close proximity to the tumor, may be required. Therefore,segmentation of the OAR may be performed when the OAR is close to thetarget tumor. In addition, if the tumor is close to the OAR (e.g.,prostate in near proximity to the bladder and rectum), segmentation ofthe OAR may allow study of the dose distribution not only in the target,but also in the OAR.

FIG. 3A illustrates a target 302 and an OAR 304, according to someembodiments of the present disclosure. It is noted that target 302 andOAR 304 shown in FIG. 3A represent a 3D reconstruction of segmentedtarget and OAR. In order to delineate the target tumor 302 from the OAR304, medical images, such as MRI images, CT images, PET images, fMRIimages, X-ray images, ultrasound images, radiotherapy portal images,SPECT images and the like, of the patient undergoing radiotherapy may beobtained non-invasively by image acquisition device 140 to reveal theinternal structure of a body part. Based on the information from themedical images, a 3D structure similar to the one shown in FIG. 3A maybe obtained. For example, the 3D structure may be obtained by contouringthe target or contouring the OAR within each 2D layer or slice of an MRIor CT image and combining the contour of each 2D layer or slice. Thecontour may be generated manually (e.g., by a physician, dosimetrist, orhealth care worker) or automatically (e.g., using a program such as theAtlas-based Autosegmentation software, ABAS®, manufactured by Elekta).In certain embodiments, the 3D structure of a target tumor or an OAR maybe generated automatically by prediction module 114.

After the target tumor and the OAR(s) have been delineated, adosimetrist, physician or healthcare worker may determine a dose ofradiation to be applied to the target tumor and any OAR proximate to thetumor (e.g., left and right parotid, optic nerves, eyes, lens, innerears, spinal cord, brain stem, and the like). After the dose isdetermined for each anatomical structure (e.g., target tumor, OAR), aprocess known as inverse planning may be performed to determine one ormore plan parameters, such as volume delineation (e.g., define targetvolumes, contour sensitive structures), margins around the target tumorand OARs, dose constraints (e.g., full dose to the tumor target and zerodose to any OAR; 95% of dose to PTV while spinal cord ≤45Gy, brain stem≤55Gy, and optic structures <54Gy), beam angle selection, collimatorsettings, and beam-on times. The result of inverse planning mayconstitute a radiation therapy treatment plan that may be stored intreatment planning system 142. Radiotherapy device 130 may then use thegenerated treatment plan having these parameters to deliver radiationtherapy to a patient.

During a treatment planning process, many parameters may be taken intoconsideration to achieve a balance between efficient treatment of thetarget tumor (e.g., such that the target tumor receives enough radiationdose for an effective therapy) and low irradiation of the OAR(s) (e.g.,the OAR(s) receives as low a radiation dose as possible). Some of theseparameters may be correlated. For example, tuning one parameter (e.g.,weights for different objectives, such as increasing the dose to thetarget tumor) in an attempt to change the treatment plan may affect atleast one other parameter, which in turn may result in the developmentof a different treatment plan.

The process of creating a treatment plan may be time consuming. Inaddition, different users (e.g., physicians, healthcare workers,dosimetrists, and the like) may prioritize parameters differently. Forinstance, different users may create different contours of the sametarget tumor or same OAR(s), use different dosage regimes for thevarious anatomies (e.g., tumor and OAR) and the like. Therefore, it maybe difficult to arrive at a consensus on an objective standard toevaluate a particular treatment plan. Under such circumstances,effective use of information (e.g., training data) derived from previoustreatments, such as statistical estimations of various parameters orrelationships among these parameters, may improve the consistency,accuracy, and efficiency of generating treatment plans.

The predictive module 114 may be used in the case of automated treatmentplanning. In this case, predictive module 114 may determine an objectiveparameter such as dose-volume histograms (DVHs). The predicted DVHs maybe used to develop a treatment plan for the actual treatment of thepatient. Alternatively, prediction module 114 may predict a first DVH,and treatment planning system 142 may determine a second DVH. Thepredicted first DVH may be compared to the second DVH as part of aquality assurance process. Thus, in some embodiments, prediction module114 may assess parameters, such as DVHs, as a safeguard to reduce thelikelihood of formulating a treatment plan that results in high levelsof radiation received by the OARs. If OARs receive too much radiationunder an initial dose plan, the initial dose plan may be rejected or mayneed to be changed to meet a desired radiation level requirement.

A DVH typically illustrates the amount of a certain volume of a target(e.g., a tumor or an OAR) that is to be irradiated with a radiation doseequal to or higher than a predetermined specific radiation value. (SeeFIG. 3B, discussed below.) For example, given a specific set of voxels Vin a target or organ (v is a voxel in V) and a dose D, DVH can bedefined as follows:

$\begin{matrix}{{{DVH}(D)} = \frac{{v \in {{V\text{:}{d(v)}} \geq D}}}{v}} & (1)\end{matrix}$

where d(v) is the actual dose in a certain voxel v and |.| denotes thetotal number of voxels in the volume V.

DVHs may also be interpreted from a probabilistic point of view. Forexample, if D denotes a specific dose and d denotes a random variable,then a cumulative distribution function can be defined asF_(D)(D)=P(d≤D), which is the probability that d is less than or equalto D. P(d≤D) can be calculated by integrating over the probabilitydensity function p_(D)(d) as follows:

P(d≤D)=∫_(−∞) ^(D) p _(D)(s)ds=∫ ₀ ^(D) p _(D)(s)ds  (2)

Further, because dose D, is always positive, the minimum must always bezero. By combining equations (1) and (2), DVHs can also be interpretedas follows:

DVH(D)=1−F _(d)(D)=1−∫₀ ^(D) p _(D)(s)ds  (3)

As discussed above, DVHs may then be used to assess the dose for aregion of interest located in different parts of the body relevant tothe treatment. FIG. 3B illustrates an embodiment of two DVHs, forexample, based on a treatment plan for a treating a target tumor and anOAR located adjacent the target tumor. A plurality of DVH curves may beprovided depending on the region of interest (e.g., prostate, head andneck, brain, lung, heart, and the like) to be irradiated. As shown inFIG. 3B, curve 302 is a DVH for an OAR, where most of the volume (e.g.,voxels) of the OAR receive less than a 5 Gy dose of radiation. Curve 304is a DVH for a target tumor (e.g., PTV), where most of the volume of thetarget tumor receives more than a 15 Gy dose of radiation. During atreatment session, the target tumor will preferably receive a high anduniform dose of radiation, according to the treatment plan, while anysurrounding healthy tissue (e.g., OAR), will preferably receive aradiation dose as small as possible.

As described above and in more detail below, the present disclosureprovides a method of using information obtained from one or moreprevious treatment plans to improve the efficiency and effectiveness ofnew treatment planning processes. The method may be carried out byradiotherapy system 100. In some embodiments, the method may include adata training process, in which training module 112 accesses trainingdatabase 122 to select data from prior treatment plans and then utilizesthe training data to generate one or more predictive models. The methodmay also include a data prediction process, in which prediction module114 utilizes testing data in conjunction with the one or more predictivemodels to predict one or more outcomes (e.g., output vectors and outputelements). The predicted outcomes may then be used to develop atreatment plan.

In some embodiments, the training process and predictive process may beincorporated into a single process, in which data flow requireco-operation of both processes. In some embodiments, the two processesmay operate separately, for example, on separate machines and/or atdifferent times, where the operation of one process does not necessarilyrequire the co-operation of the other process. In such embodiments, datasharing between the two processes may use a database, or maybe performedin an off-line mode.

Data Training

FIG. 4 is a flowchart of a method of data training and data prediction,according to some embodiments of the present disclosure. FIG. 4 includestwo processes: a data training process 400 and a data prediction process420. As described above, data training process 400 and data predictionprocess 420 may be incorporated into a single process or may be separateprocesses. In some embodiments, data training process 400 may beimplemented by training module 112. Similarly, data prediction process420 may be implemented by prediction module 114.

Data training process 400 may include a step 402, in which trainingmodule 112 may receive training data from training database 122. Thetraining data may include a plurality of previous treatment plans thatare stored in training database 122. For example, the stored trainingdata may include diagnostic images, treatment images (dose maps),segmentation information, and the like, associated with one or moreprevious treatment plans. The training data may include a plurality oftraining samples. Each training sample may comprise a feature vector anda corresponding output vector.

The feature vector may include one or more feature elements. Eachfeature element may indicate an observation of a medical image (e.g.,provided by image acquisition device 140 or stored in training database122) used in a past radiotherapy session. The observation may be adistance between a volume (e.g., a voxel) and an anatomical region, suchas a target or the surface of the body part in the medical image. Inanother example, the observation may include spatial coordinates of ananatomical region or a probability that an anatomical region includes aparticular tissue type. In another example, the feature element mayinclude patient specific information, responsible physician, organ orvolume of interest segmentation data, functional organ modeling data(e.g., serial versus parallel organs, and appropriate dose responsemodels), radiation dosage (e.g., also including dose-volume histogram(DVH) information), lab data (e.g., hemoglobin, platelets, cholesterol,triglycerides, creatinine, sodium, glucose, calcium, weight), vitalsigns (blood pressure, temperature, respiratory rate and the like),genomic data (e.g., genetic profiling), demographics (age, sex), otherdiseases affecting the patient (e.g., cardiovascular or respiratorydisease, diabetes, radiation hypersensitivity syndromes and the like),medications and drug reactions, diet and lifestyle (e.g., smoking ornon-smoking), environmental risk factors, tumor characteristics(histological type, tumor grade, hormone and other receptor status,tumor size, vascularity cell type, cancer staging, gleason score),previous treatments (e.g., surgeries, radiation, chemotherapy, hormonetherapy), lymph node and distant metastases status, genetic/proteinbiomarkers (e.g., such as MYC, GADD45A, PPM1D, BBC3, CDKN1A, PLK3, XPC,AKT1, RELA, BCL2L1, PTEN, CDK1, XIAP, and the like), single nucleotidepolymorphisms (SNP) analysis (e.g., XRCC1, XRCC3, APEX1, MDM2, TNFR,MTHFR, MTRR, VEGF, TGFβ, TNFα), and the like. The feature vector mayinclude one or more such feature elements, regardless of whether thesefeature elements are related to each other or not.

The output vector may include one or more output elements. Each outputelement may indicate a corresponding plan outcome or parameter in thepast radiotherapy session based on the observation(s) included in thefeature vector. For example, the output element may include the doseapplied or received at a particular spatial location (e.g., a voxel). Inanother example, the output element may include a patient survival timebased on observations such as a treatment type, treatment parameters,patient history, and/or patient anatomy. Additional examples of outputelements include, but not limited to, a normal tissue complicationprobability (NTCP), a region displacement probability during treatment,or a probability that a set of coordinates in a reference image ismapped to another set of coordinates in a target image. The outputvector may include one or more such output elements, regardless ofwhether these output elements are related to each other or not.

As an example of an embodiment, an output element may include a dose tobe applied to a voxel of a particular OAR. Further, a feature elementmay be used to determine the output element. The feature element mayinclude a distance between the voxel in the OAR and the closest boundaryvoxel in a target tumor. Therefore, the feature element may include asigned distance x indicating the distance between a voxel in an OAR andthe closest boundary voxel in a target for the radiation therapy. Theoutput element may include a dose D in the voxel of the OAR from which xis measured. In some other embodiments, each training sample maycorrespond to a particular voxel in the target or OAR, such thatmultiple training samples within the training data corresponding to thewhole volume of the target or OAR and other anatomical portions subjectto the radiotherapy treatment.

At step 404, training module 112 may determine a joint probabilitydensity associated with a feature vector and a corresponding outputvector based on the training data. The joint probability density mayindicate a likelihood that both an observation indicated by the featurevector and a plan outcome indicated by the corresponding output vectorare present in the training data.

For example, the feature vector may include a single element, such asassigned distance x, and the corresponding output vector may include asingle output element, such as the dose D. Training module 112 maydetermine a joint probability density p(x, D) using a density estimationmethod such as a Kernel Density Estimation (KDE) algorithm. KDE is anon-parametric algorithm that applies a kernel function to each datapoint and then sums the kernels. A kernel may be defined as a functionsatisfying the following properties:

∫κ(x)dx=1,∫xκ(x)dx=0,∫x ²κ(x)dx>0.

Specifically, KDE is used to make an estimate f′(x) of a densityfunction f(x) for some parameter x given N observations x_(i) and akernel κ_(h)(x). An univariate KDE (e.g., one dimensional KDE) can bewritten as follows:

$\begin{matrix}{{f^{\prime}(x)} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}{\kappa_{h}\left( {x - x_{i}} \right)}}}} & (6)\end{matrix}$

where h denotes the bandwidth parameter and

${\kappa_{h}(x)} = {\frac{1}{h}{{\kappa \left( \frac{x}{h} \right)}.}}$

A KDE of a joint probability distribution f′(x), where x=(x₁, x₂)^(T) isgenerally defined as:

$\begin{matrix}{{f^{\prime}(x)} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}{\kappa_{H}\left( {x - x_{i}} \right)}}}} & (7)\end{matrix}$

where the ith observation x_(i)=(x_(1i), x_(2i))^(T) and

κ_(H)(x−x _(i))=det(H)^(−1/2)κ(H ^(−1/2)(x−x _(i)))  (8)

$H = \begin{pmatrix}h_{1}^{2} & h_{12} \\h_{12} & h_{2}^{2}\end{pmatrix}$

is a symmetric and positive-definite matrix.

In some embodiments, H may be simplified to a diagonal matrix. Then theKDE can be expressed as

$\begin{matrix}{{f^{\prime}(x)} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}{{\kappa_{h}\left( {x_{1} - x_{1\; i}} \right)}{\kappa_{h}\left( {x_{2} - x_{2\; i}} \right)}}}}} & (9)\end{matrix}$

where h=√{square root over (h₁h₂)} and h₁ ² and h₂ ² can be identifiedas the diagonal elements in H. Thus, training module 112 may use the KDEalgorithm to determine a joint probability density p(x, D) when thefeature element includes the signed distance x and the correspondingoutput element includes the dose D.

In some embodiments of training process 400, step 406 and step 408,described below, may be optional. Therefore, in one embodiment, trainingprocess 400 may include steps 402, 404, and 410. In another embodiment,training process may include steps 402, 404, 406, 408, and 410.

At step 406, training module 112 may determine a probability densityassociated with the feature vector, or each element within the featurevector, based on the training data. The probability density may indicatea likelihood that the observation indicated by the feature vector, oreach element (e.g., the signed distance x) within the feature vector, ispresent in the training data. For example, when the feature vectorincludes the signed distance x, training module 112 may determine theprobability density p(x) for the entire training data using aone-dimensional KDE algorithm, as described above.

At step 408, training module 112 may determine a conditional probabilitydensity (e.g., P(y|x)) associated with a feature vector (e.g., vector x)and the corresponding output vector (e.g., vector y) based on thedetermined joint probability (e.g., P(y,x)=P(y|x)p(x)) and thedetermined probability density (e.g., P(x)) associated with the featurevector x. In some embodiments, feature vector x may correspond to adistance, a spatial coordinate, patient specific information, and thelike. In some embodiments, output vector y may correspond to a dose, atumor control probability, a normal tissue complication probability, apatient survival time, a region displacement probability duringtreatment, and the like.

A feature vector may include a plurality of feature elements (e.g.,x=[x1, x2, x3, . . . ]), and an output vector may contain a plurality ofoutput elements (e.g., y=[y1, y2, y3, . . . ]). In one embodiment,training module 112 may determine a joint probability distribution basedon all the feature elements and all the output elements. In anotherembodiment, training module 112 may determine a joint probabilitydistribution based on all the feature elements and each output element.

The conditional probability density may indicate a likelihood that theplan outcome indicated by the corresponding output vector or element(e.g., the dose D) is present in the training data given a presence ofthe feature vector or element (e.g., the signed distance x) in thetraining data. For example, when the feature vector includes the signeddistance x and the corresponding output vector includes the dose D,training module 112 may determine the conditional probability density ofthe dose D given a distance x as follows:

$\begin{matrix}{{p\left( D \middle| x \right)} = \frac{p\left( {x,D} \right)}{p(x)}} & (10)\end{matrix}$

As described above, using a distance x to determine a probabilitydensity of a dose D, is one embodiment where both the input and theoutput are single scalar variables. In other embodiments, a model may becreated to use multiple variables for x, (such as a plurality ofdistance coordinates to an OAR, a plurality of distance coordinates to atumor, spatial coordinates, tissue probabilities, information fromoriginal images, information from post-processed images) to estimate theprobability density for a particular variable y (e.g., determine theprobability density of a dose or determine a probability density of atumor control probability and the like). Therefore, in an embodiment, aprobability density of a tumor control probability based on multipletissue probabilities may be determined (e.g., P(y|x₁, x₂, x3, . . . )).

The training of the one or more predictive models may be performedeither offline or online. For example, the joint probability may beestimated and stored before beginning the treatment process (e.g.,offline) or the joint probability may be estimated in real-time duringthe treatment process (e.g., online). In another embodiment, treatmentplans that differ significantly from prior treatment plans can bedetected. In this case, the training process may be conducted offline.

In an embodiment, the training process may or may not use data fromother patients. In some embodiments, training data may be used to adaptthe treatment plan for the patient by comparing a plurality of previoustreatment plans developed for the same patient. In another embodiment,training data may include treatment plans from a plurality of otherpatients with the same or similar medical diagnosis.

Generate Predictive Model

At step 410, training module 112 may store the conditional probability(e.g., P(y|x)), which may constitute the result of training process 400,in training database 122. For example, training module 112 may store oneor more conditional probabilities (e.g., p(D|x)), where D is dose) intraining database 122 as a predictive model. In an embodiment, trainingmodule 112 may store one or more joint probabilities (e.g., p(y,x)) intraining database 122 as a predictive model. Thus, the predictive modelmay be determined by using either the one or more joint probabilities orthe one or more conditional probabilities.

As described above, this predictive model may indicate informationobtained or derived from one or more past radiotherapy sessions. Forexample, the predictive model may include statistical estimations ofparameters used in the past radiotherapy sessions. The predictive modelmay also include statistical estimations of relationships amongparameters used in the past radiotherapy sessions. The predictive modelmay also include statistical estimations of outcomes of the pastradiotherapy sessions.

Apply Predictive Model(s) to Testing Data

Once the predictive models are obtained, they can be used in predictionprocess 420 to predict the probability density associated with theoutput vector, or an output element, for the development of a newtreatment plan.

At step 422, prediction module 114 may receive testing data including aplurality of testing samples. In some embodiments, testing data andtesting samples may be similar to training data and training samplesdescribed above. Each testing sample may include a feature vector, whichmay include one or more feature elements, Each testing sample mayinclude an output vector, which may include one or more output elements.For example, while training data and training samples may relate toprevious or past treatments, testing data may relate to a new patient,or a new treatment session of the same patient. For instance, theobservation of a medical image used in a past treatment session may formpart of the feature vector in the training data. On the other hand,observation of a medical image used in the new treatment session mayform part of the feature vector in the testing data. In other words,while the formats of the training data and the testing data may besimilar, training data may relate to past treatments and testing datamay relate to the new treatment.

At step 424, prediction module 114 may receive one or more predictivemodels from training module 112. Testing data may be used in conjunctionwith the one or more predictive models to predict objective parametersof one or more outcomes. To predict the objective parameters (e.g.,feature vectors, feature elements, output vectors, and output elements),testing data may be applied to the one or more predictive models. Thepredicted outcomes may then be used to develop a treatment plan.

For example, when the feature vector includes the signed distance x andthe corresponding output vector includes the dose D, the conditionalprobability density may be p(D|x) as determined at step 408 and storedat step 410. Alternatively, when the feature vector includes spatialcoordinates and tissue probabilities, the corresponding output vectormay be a region displacement probability during treatment (e.g.,p(r_(x),r_(y),r_(z)|x,y,z,t), where (r_(x),r_(y),r_(z)) is the regiondisplacement vector given spatial coordinates (x,y,z) and a tissueprobability t).

At step 426, prediction module 114 may determine a probability densityassociated with the feature vector of each testing sample based on thetesting data. The probability density may indicate a likelihood that anobservation indicated by the feature vector is present in the testingdata. For example, when the feature vector includes the signed distancex, prediction module 114 may estimate the probability density p*(x) forthe new plan. In an embodiment, the feature vector may be treated as asequence of Dirac pulses, denoted as δ(x), in order to estimate theprobability density p(x*):

$\begin{matrix}{{p\left( x^{*} \right)} = {\frac{1}{s}{\sum\limits_{s \in S}{\delta \left( {x - X_{s}^{*}} \right)}}}} & (13)\end{matrix}$

where |S| denotes the number of elements in S.

In some embodiments, the feature vector may include arbitrary dimensionand/or multiple types of data (e.g., continuous, ordinal, discrete, andthe like). In some embodiments, images used in the training process andpredicting processes may include diagnostic images, treatment images(dose maps), and/or segmentation images. In some embodiments, thefeature vector may include a distance to predetermined anatomicalregions, such as the target or the OAR(s) or the patient surface. Suchinformation may be summarized using an Overlap-Volume Histogram.Distances to multiple regions of interest may also be included in thefeature vector. In some embodiments, the feature vector may includeglobal information, such as spatial coordinates and/or tissueprobabilities. In some embodiments, the feature vector may includefeatures derived from a convolution of images with at least one linearfilter (e.g., local phase, gradients, edge, or corner detectors). Insome embodiments, the feature vector may include features derived by atransformation of one or more images (e.g., Fourier transform, Hilberttransform, Radon transform, distance transform, discrete cosinetransform, wavelet transform). In each of these embodiments describedabove regarding the feature vector, a corresponding transformation tothe output probability density may be applied.

In some embodiments, the feature vector may include information based on“information theoretical measures” (e.g., mutual information, normalizedmutual information, entropy, Kullback-Leibler distance, and the like).In some embodiments, the feature vector may include a feature descriptorproviding a higher-dimensional representation as utilized in the fieldof computer vision, such feature descriptor may include characteristicsof a particular voxel of the image, such as SIFT (Scale-invariantfeature transform), SURF (Speeded Up Robust Features), GLOH (GradientLocation and Orientation Histogram), or HOG (Histogram of OrientedGradients). In another embodiment, the covariance/correlation between aplurality of image regions (e.g., two or more voxels) can be capturedusing a higher-dimensional representation. In some embodiments, thefeature vector may include, for example, patient information such asage, gender, tumor size, a responsible physician and the like.

At step 428, prediction module 114 may determine a prediction of aprobability density associated with an output vector, or its elements,based on the probability densities determined for the plurality oftesting samples and the one or more predictive models. Specifically, theoutput vector can be used to detect one or more important features for afavorable outcome. The prediction of the probability density mayindicate a likelihood of a plan outcome indicated by the output vectoror its element(s) to be made in the new treatment plan.

In some embodiments, the output vector may include probabilitydistribution of arbitrary dimensions; the dose; the tumor controlprobability (TCP) or normal tissue complication probability (NTCP),either on a micro- or macro-scale; or patient survival time based on,for example, treatment type, treatment parameters, patient history, oranatomy.

In some embodiments, the determination of the prediction of theprobability density associated with the output vector may be carried outin a spatial domain. In some embodiments, this process may be performedin the frequency domain (e.g., Fourier domain, native to MRIacquisition). In some embodiments, the process may be performed in Radontransform space, for example, native to CT acquisition. In someembodiments, the prediction may be used on compressed images that use,for example, wavelet transforms, and the process may be performed inwavelet transform space.

The predicted probability distribution may be used to derivepoint-estimates and corresponding measures of variation, expressed, forexample, as the moments of the probability distribution. In oneembodiment, an estimated spatial dose map may be computed by taking themean (i.e., first moment) of the distribution. In another embodiment,the spatial dose variability may be represented by the standarddeviation (i.e., square root of the central second moment).

In some embodiments, the output vector may be used to predict theprobability density of various characteristics. For instance, the outputvector may include the probability that the anatomical region ofinterest may move during treatment, such as the lungs, heart, orprostate. In some embodiments, the output vector (e.g., a 3D vector) maybe used to guide deformable registration by modeling correlations of apatient's anatomy, for example, the output vector may include theprobability that a set of coordinates in an atlas image is mapped toanother set of coordinates in a target image, or vice versa. In someembodiments, the output vector may be sampled by Monte Carlo simulationsof radiation transport and used to speed up calculations in subsequentdose calculations. In some embodiments, the predicted probabilitydensity may be used to detect outliers in commissioning of radiotherapysystems.

The prediction process 420 may allow for the development of a treatmentplan, at step 430, by utilizing a plurality of objective parameters ofone or more outcomes. For example, prediction module 114 may determine adose-volume histogram (DVH) based on the determined prediction of theprobability density associated with the output element. In this case,the feature vector includes the signed distance x and the correspondingoutput vector includes the dose D. The prediction of the probabilitydensity p*(D) may be determined as follows:

p*(D)=∫p(D|x)p*(x)dx  (11)

and the corresponding DVH(D) may be calculated as follows:

DVH(D)=1−∫₀ ^(D) p*(s)ds  (12).

In an embodiment, at step 430, the prediction process 420 may allow forthe validation of a treatment plan, e.g., for the purposes of qualityassurance or training. For example, step 430 may allow for thevalidation of one or more previously generated treatment plans with thenewly generated treatment plan.

FIG. 5 is a flowchart of another embodiment that utilizes patientspecific testing data to select training data to perform a trainingprocess 500 and a data prediction process 520. In some embodiments,processes 500 and 520 may be similar to processes 400 and 420 aspreviously described with respect to FIG. 4, respectively, with somedifferences that will be described below. Referring to FIG. 5, testingdata 501 may be used in training process 500 and prediction process 520.In some embodiments, testing data 501 may include a plurality of testingsamples. In some embodiments, testing data 501 and its testing samplesmay be similar to the testing data received at step 424 in FIG. 4. Asnoted above, FIG. 5 may be similar to FIG. 4. The differences betweenFIG. 4 and FIG. 5 include the way patient specific testing data (e.g.,testing data 501) are utilized. For example, referring to FIG. 5,training process 500 may utilize patient specific testing data 501 togenerate one or more predictive models. In some embodiments, patientspecific testing data 501 may be used at step 502, where training module110 may select a subset of training data from training database 122based on testing data 501.

At step 504, training module 112 may determine a joint probabilitydensity associated with a feature vector and a corresponding outputvector based on the training data selected at step 502. Step 504 may besimilar to step 404 except the training data used in step 504 may beselected based on testing data 501.

In some embodiments of training process 500, step 506 and step 508,described below, may be omitted. Therefore, in one embodiment, trainingprocess 500 may include steps 502, 504, and 510. In another embodiment,training process may include steps 502, 504, 506, 508, and 510.

At step 506, probability density associated with a feature vector may bedetermined, similar to step 406. At step 508, a conditional probabilityassociated with an output vector given a feature vector may bedetermined, similar to step 408.

At step 510, training module 112 may store one or more conditionalprobabilities in training database 122 as a predictive model. In someembodiments, training module 112 may store one or more jointprobabilities in training database 122 as a predictive model. Thus, thepredictive model may be determined based on either the one or more jointprobabilities or the one or more conditional probabilities.

Once the predictive model(s) specific for a particular patient aregenerated by training module 112 (e.g., through training process 500),testing data 501 may be further used by prediction module 114 inconjunction with the one or more predictive models to predict one ormore outcomes (e.g., output vectors or output elements).

For example, at step 522, testing data 501 may be received and used inprediction process 520 along with the one or more predictive modelsreceived from training module 112 (e.g., received at step 524).

At step 526, prediction module 114 may determine a probability densityassociated with the feature vector of each testing sample based on thetesting data, similar to step 426.

At step 528, prediction module 114 may determine a prediction of aprobability density associate with the output vector, or its elements,based on the probability densities determined for the plurality oftesting samples and the one or more predictive models, similar to step428.

Once the predicted outcomes are generated, the predicted outcomes maythen be used either to develop a treatment plan or to validate apreviously generated treatment plan, at step 530.

In some embodiments, a plurality of testing data may be received fromtreatment planning system 142, either in an online mode or an offlinemode.

In addition to the KDE method described above, other density estimationmethods may also be used to determine the joint probability densityassociated with a training sample or the probability density associatedwith the feature vector of a testing sample. Examples of densityestimation methods include, but not limited to:

Non-parametric methods—non-parametric methods may estimate the densitywith minimum assumptions. Examples include the KDE described above, andartificial neural networks, which model the unknown function as aweighted sum of several sigmoids, each of which is a function of all therelevant explanatory variables.

Parametric methods—parametric methods assume a parameterized probabilitydistribution and fit it to the data. An example is the Gaussian mixturemodel.

Monte Carlo based methods—this type of methods uses repeated randomsampling to estimate a probability distribution and can therefore beused in limited instances such as simulations.

Machine learning methods—machine learning method can be extended toperform density estimation. Examples include transductive support vectormachines (SVM), decision forests, random forests, regression models, anddensity estimation trees. Some of the density estimation methods may beparticularly suitable for outlier detection or relevance determination,such as density estimation trees. Monte Carlo based methods and somemachine learning methods, such as density estimation trees, may bebetter equipped to handle high-dimensional data.

Various operations or functions are described herein, which may beimplemented or defined as software code or instructions. Such contentmay be directly executable (“object” or “executable” form), source code,or difference code (“delta” or “patch” code). Software implementationsof the embodiments described herein may be provided via an article ofmanufacture with the code or instructions stored thereon, or via amethod of operating a communication interface to send data via thecommunication interface. A machine or computer readable storage mediummay cause a machine to perform the functions or operations described,and includes any mechanism that stores information in a form accessibleby a machine (e.g., computing device, electronic system, and the like),such as recordable/non-recordable media (e.g., read only memory (ROM),random access memory (RAM), magnetic disk storage media, optical storagemedia, flash memory devices, and the like). A communication interfaceincludes any mechanism that interfaces to any of a hardwired, wireless,optical, and the like, medium to communicate to another device, such asa memory bus interface, a processor bus interface, an Internetconnection, a disk controller, and the like. The communication interfacecan be configured by providing configuration parameters and/or sendingsignals to prepare the communication interface to provide a data signaldescribing the software content. The communication interface can beaccessed via one or more commands or signals sent to the communicationinterface.

The present invention also relates to a system for performing theoperations herein. This system may be specially constructed for therequired purposes, or it may comprise a general-purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program may be stored in a computerreadable storage medium, such as, but not limited to, any type of diskincluding floppy disks, optical disks, CDROMs, and magnetic-opticaldisks, read-only memories (ROMs), random access memories (RAMs), EPROMs,EEPROMs, magnetic or optical cards, or any type of media suitable forstoring electronic instructions, each coupled to a computer system bus.

The order of execution or performance of the operations in embodimentsof the invention illustrated and described herein is not essential,unless otherwise specified. That is, the operations may be performed inany order, unless otherwise specified, and embodiments of the inventionmay include additional or fewer operations than those disclosed herein.For example, it is contemplated that executing or performing aparticular operation before, contemporaneously with, or after anotheroperation is within the scope of aspects of the invention.

Embodiments of the invention may be implemented with computer-executableinstructions. The computer-executable instructions may be organized intoone or more computer-executable components or modules. Aspects of theinvention may be implemented with any number and organization of suchcomponents or modules. For example, aspects of the invention are notlimited to the specific computer-executable instructions or the specificcomponents or modules illustrated in the figures and described herein.Other embodiments of the invention may include differentcomputer-executable instructions or components having more or lessfunctionality than illustrated and described herein.

When introducing elements of aspects of the invention or the embodimentsthereof, the articles “a,” “an,” “the,” and “said” are intended to meanthat there are one or more of the elements. The terms “comprising,”“including,” and “having” are intended to be inclusive and mean thatthere may be additional elements other than the listed elements.

Having described aspects of the invention in detail, it will be apparentthat modifications and variations are possible without departing fromthe scope of aspects of the invention as defined in the appended claims.As various changes could be made in the above constructions, products,and methods without departing from the scope of aspects of theinvention, it is intended that all matter contained in the abovedescription and shown in the accompanying drawings shall be interpretedas illustrative and not in a limiting sense.

1.-23. (canceled)
 24. A radiotherapy system for treating a targetpatient according to a treatment plan, the system configured to usetraining data associated with past treatment plans used to treat samplepatients, the training data including a plurality of observationsassociated with conditions of the sample patients and derived frommedical image data, the training data further including one or more planoutcomes reflecting outcomes resulting from the past treatment plans, orplan parameters reflecting design parameters of the past treatmentplans, the system comprising: a processing device configured to generatethe treatment plan, the data processing device configured to executeinstructions stored on a computer-readable medium that, upon execution,cause the processing device to perform operations including: determininga joint probability density indicating a likelihood that both at leastone particular observation and at least one particular plan outcome orplan parameter are present in the training data; calculating aconditional probability based upon the determined joint probabilitydensity, wherein the conditional probability indicates a likelihood thatthe particular plan outcome or plan parameter is present in the trainingdata; predicting a probability of a patient specific plan outcome orplan parameter based on the conditional probability and at least onepatient specific observation associated with the target patient andderived from medical image data; generating the treatment plan based onthe prediction; and controlling a radiotherapy device to perform theradiotherapy treatment according to the generated treatment plan. 25.The radiotherapy system of claim 24, wherein the training data comprisea plurality of training samples.
 26. The radiotherapy system of claim24, wherein the training data comprise a plurality of images.
 27. Theradiotherapy system of claim 26, wherein the training data comprise atraining sample and the training sample includes characteristics of avoxel in an image.
 28. The radiotherapy system of claim 24, wherein thepast treatment plans are from a current patient, a plurality of otherpatients, or a combination thereof.
 29. The radiotherapy system of claim24, wherein the past treatment plans are from at least one of a singlepatient or a plurality of patients.
 30. The radiotherapy system of claim26, wherein the plurality of images comprise at least one of a MagneticResonance Imaging (MRS) image, a 3D MRI image, a 2D streaming MRI image,a 4D volumetric MRI image, a Computed Tomography (CT) image, a Cone-BeamCT image, a Positron Emission Tomography (PET) image, a functional MRI(fMRI) image, an X-ray image, a fluoroscopic image, an ultrasound image,a radiotherapy portal image, or a single-photo emission computedtomography (SPECT) image.
 31. The radiotherapy system of claim 24,wherein the instructions additionally cause the processing device tocalculate a probability that the particular observation is present inthe training data; and wherein the conditional probability indicates alikelihood that the particular plan outcome or plan parameter is presentin the training data given the probability that the particularobservation is present in the training data.
 32. The radiotherapy systemof claim 24, wherein determining the joint probability density orcalculating the conditional probability comprises using at least one ofa non-parametric method, a parametric method, a Monte Carlo basedmethod, a regression method, a machine learning method, or combinationsthereof.
 33. A method for treating a target patient according to atreatment plan and for use with training data associated with pasttreatment plans used to treat sample patients, the training dataincluding a plurality of observations associated with conditions of thesample patients and derived from medical image data, the training datafurther including one or more plan outcomes reflecting outcomesresulting from the past treatment plans, or plan parameters reflectingdesign parameters of the past treatment plans, the method comprising:determining a joint probability density indicating a likelihood thatboth at least one particular observation and at least one particularplan outcome or plan parameter are present in the training data;calculating a conditional probability based upon the determined jointprobability density, wherein the conditional probability indicates alikelihood that the particular plan outcome or plan parameter is presentin the training data; predicting a probability of a patient specificplan outcome or plan parameter based on the conditional probability andat least one patient specific observation associated with the targetpatient and derived from medical image data; generating the treatmentplan based on the prediction; and controlling a radiotherapy device toperform the radiotherapy treatment according to the generated treatmentplan.
 34. The method of claim 33, wherein determining the jointprobability density comprises using at least one of a non-parametricmethod, a parametric method, a Monte Carlo based method, a regressionmethod, a machine learning method, or combinations thereof.
 35. Themethod of claim 33, wherein determining the conditional probabilitycomprises using at least one of a non-parametric method, a parametricmethod, a Monte Carlo based method, a regression method, a machinelearning method, or combinations thereof.
 36. The method of claim 33,further comprising: receiving patient specific testing data, the patientspecific testing data including at least one of imaging data, organ orvolume of interest segmentation data, functional organ modeling data,radiation dosage, laboratory data, genomic data, demographics, otherdiseases affecting the patient, medications and drug reactions, diet andlifestyle, environmental risk factors, tumor characteristics,genetic/protein biomarkers, or previous medical treatments of thepatient.
 37. The method of claim 33, further comprising: receivingpatient specific testing data associated with the target patient, thepatient specific testing data including a patient specific featurevector, wherein the patient specific feature vector includes at leastone patient specific observation associated with the target patient,wherein the at least one patient specific observation is derived frommedical image data.
 38. The method of claim 33, further comprising:receiving patient specific testing data associated with the targetpatient, the patient specific testing data including a patient specificfeature vector, wherein the patient specific feature vector includes atleast one patient specific observation associated with the targetpatient, wherein the feature vector comprises at least one of a distanceto an anatomical region of interest, a tissue probability, a pluralityof spatial coordinates, information derived from a convolution of imageswith at least one linear filter, information derived from a convolutionof images with at least one non-linear filter, information derived froma transformation of one or more images, information based on theoreticalmeasures, a feature descriptor of a type used in computer vision, atumor size, a tumor type, a tumor location, a patient's age, a patient'sgender, a patient's ethnicity, a patient's body-weight-index (BMI),patient information, or information of a responsible physician.
 39. Themethod of claim 33, wherein the training data associated with pasttreatment plans includes a plurality of training samples, each of thetraining samples including a feature vector and an output vectorcorresponding to the feature vector.
 40. The method of claim 39, whereinthe output vector comprises at least one of a dose, a tumor controlprobability (TCP), a normal tissue complication probability (NTCP), apatient survival time, a region displacement probability duringtreatment, or a probability that a set of coordinates in a referenceimage is mapped to another set of coordinates in a target image.
 41. Themethod of claim 33, wherein the past treatment plans are from a currentpatient, a plurality of other patients, or a combination thereof. 42.The method of claim 33, comprising: selecting the training data from asubset of all available training data based on the patient specifictesting data.
 43. The method of claim 33, wherein the past treatmentplans are from a single patient.
 44. The method of claim 33, wherein thepast treatment plans are from a plurality of patients.
 45. The method ofclaim 33, further comprising: receiving the training data, the trainingdata including a plurality of training samples, each of the trainingsamples including a feature vector and an output vector corresponding tothe feature vector, wherein: the feature vector includes one or moreobservations associated with conditions of the sample patients, whereinthe one or more observations are derived from medical image data; andthe output vector includes one or more plan outcomes reflecting outcomesresulting from the past treatment plans, or plan parameters reflectingdesign parameters of the past treatment plans.